Collaborating Authors


GitHub - anshkumar/yolact: Tensorflow 2.x implementation YOLACT


This is a Tensorflow 2.3 implementation of the paper YOLACT: Real-time Instance Segmentation and YOLACT: Better Real-time Instance Segmentation. The paper presents a fully-convolutional model for real- time instance segmentation that achieves 29.8 mAP on MS COCO at 33.5 fps evaluated on a single Titan Xp, which is significantly faster than any previous competitive approach. Unlike original implemetation of YOLACT/YOLACT in which image is resized to 550x550, this repo can handle image of size MxN. For detailed steps to install Tensorflow, follow the Tensorflow installation instructions. The remaining libraries can be installed on Ubuntu 16.04 using via apt-get: The default metrics are based on those used in Pascal VOC evaluation.

How Microsoft plans to improve the low-code landscape


Taking on the challenges head-on that stand in the way of their low-code platforms growing, Microsoft's series of new product announcements this week at Build 2022 gives organizations new options for achieving low-code development goals. Microsoft's series of low-code announcements made this week include Power Pages, the latest Microsoft Power Platform addition for creating integrated, scalable and secure websites. Lured by the promises of democratizing app development with visual, declarative, drag and drop interfaces often bundled with enterprise-wide platforms like Microsoft, Salesforce, ServiceNow and others, enterprises have been quick to jump in and experiment. They're learning that support for a low-code platform can get expensive fast once app development moves from small department coding projects to larger-scale, enterprise-wide apps. Low-code platforms' hidden costs include limited process workflow support that further adds to the challenge of scaling them enterprise-wide.

Machine Learning On VMware Cloud Platform - AI Summary


The stack runs a machine learning model inside a container or a VM, preferably onto an accelerator device like a general-purpose GPU. Using self-service marketplace services, such as "VMware Application Catalog" (formerly known as Bitnami), allows IT organizations to work together with the head of data science to curate their ML infrastructure toolchains. The key to convincing the data science teams is understanding the functional requirements of the phases of the model development lifecycle and deploying an infrastructure that can facilitate those needs. As you can imagine, a collection of bare metal machines assigned to individual data scientists or teams with dedicated expensive GPUs might be overkill for this scenario. Still, if the data science team wants to research the effect and behavior of the combination of the model and the GPU architecture, virtualization can be beneficial.

Synamedia Acquires Utelly To Boost Synamedia Go's Content Discovery Capabilities


Synamedia, the world's largest independent video software provider, announced the acquisition of Utelly, a UK-based privately-owned content discovery platform provider with products targeted at the entertainment industry. Its offerings include metadata aggregation, search and recommendations, as well as content management and a content promotion engine. Its SaaS-based technology is already pre-integrated with the Synamedia Go video platform and will now be embedded in the Go.Aggregate add-on pack to solve one of the major challenges viewers face: finding content across TV and apps on any screen. Utelly's technology achieves this through metadata aggregation, intelligent asset linking, AI and machine learning. By unifying data and using AI to enrich sparse data sets, Utelly provides customers with search and recommendations that enhance viewers' content discovery experiences.

Machine Learning in Python for Cryptocurrency Trading


It is a comprehensive course that shows how you can build a stylish web app with machine learning at the backend to predict the future price of any cryptocurrency. The main course has a mini crash course on Python for newbies and culminates into the theory and practice of Machine Learning and its predictive modeling application on cryptocurrencies. At the end of this course, you will be able to develop a full-fledged web app that will take in data (available for free on the Internet). As you will provide the data to the web app, the web app having its predictive machine learning model at the backend will spit out the future prices of a cryptocurrency. The course includes all the code for the web app, and with a tiny tuning in the code, you can adjust the web app to predict the prices of any cryptocurrency.

Event-Driven Scalability in Data Processing Pipeline


Building a data processing pipeline is one of the most common problem statements, for which you would have written small scripts or built a full-fledged scalable system based on the amount, and frequency of data. In this article, we will talk about the idea of event-driven scalability, the backbone that will be cost-optimized, and requires a minimum amount of development and operations. Why build an event-driven and scalable data processing pipeline? While working with startups or building a team project or a personal project, that requires a pipeline for data processing, there is always a constraint of cost. We will use a simple example for building a metadata extraction system for e-commerce products.

How AI makes developers' lives easier, and helps everybody learn to develop software


Ever since Ada Lovelace, a polymath often considered the first computer programmer, proposed in 1843 using holes punched into cards to solve mathematical equations on a never-built mechanical computer, software developers have been translating their solutions to problems into step-by-step instructions that computers can understand. Today, AI-powered software development tools are allowing people to build software solutions using the same language that they use when they talk to other people. These AI-powered tools translate natural language into the programming languages that computers understand. "That allows you, as a developer, to have an intent to accomplish something in your head that you can express in natural language and this technology translates it into code that achieves the intent you have," Scott said. "That's a fundamentally different way of thinking about development than we've had since the beginning of software."

Artificial intelligence investment grows, but barriers remain


While the potential of machine learning and AI has helped address several problems across many industries, it has also created an acute imbalance in the supply and demand of AI talent, says Oliver Tavakoli, CTO at Vectra. Cybersecurity companies have to deal with this shortage as they compete with major organizations for talent and "have resorted to AI-as-a-sidecar (solving a small number of peripheral problems through the application of AI) rather than AI-as-the-engine (building the core of their offerings around AI and solving peripheral problems with conventional techniques). Predictable, the former approach has resulted in a large gap in what they deliver vs. the value customers think the AI should be delivering," Tavakoli explains.

The Beginner's Guide to Artificial Intelligence in Unity 2022 - Couponos


The course begins with a detailed examination of vector mathematics that sits at the very heart of programming the movement of NPCs. Following this, systems of waypoints will be used to move characters around in an environment before examining the Unity waypoint system for car racing with AI controlled cars. This leads into an investigation of graph theory and the A* algorithm before we apply these principles to developing navmeshes and developing NPCs who can find their way around a game environment. Before an aquarium is programmed complete with autonomous schooling fish, crowds of people will be examined from the recreation of sidewalk traffic, to groups of people fleeing from danger. Having examined the differing ways to move NPCs around in a game environment, their thinking abilities will be discussed with full explanations and more hands-on workshops using finite state machines and behaviour trees.